GPT-5.6 Terra
gpt-5-6-terra
70% in · 30% out mix
Higher = better value
Speed
86/100
Context
1.1M
Tier
smart
Gemini 3.1 Pro
gemini-3-1-pro
70% in · 30% out mix
Higher = better value
Speed
82/100
Context
2.0M
Tier
smart
IN-DEPTH ANALYSIS
GPT-5.6 Terra vs Gemini 3.1 Pro: Detailed Comparison
GPT-5.6 Terra is OpenAI's mid-range-tier language model with a 1.1M-token context window, excelling at reasoning. Gemini 3.1 Pro from Google is a mid-range-tier model supporting 2.0M tokens in context, with standout performance in vision/multimodal.
Price is not the deciding factor here. GPT-5.6 Terra and Gemini 3.1 Pro land within 0% of each other on a typical prompt/completion mix, which is inside the margin your own input/output ratio will move anyway. Pick on capability, context, or latency instead — the monthly bill will look much the same either way. GPT-5.6 Terra is priced at $2.00/M input tokens and $12.00/M output tokens. Gemini 3.1 Pro costs $2.00/M input and $12.00/M output.
In independent benchmark evaluations, GPT-5.6 Terra leads with coding scores of 93/100 and reasoning scores of 94/100, compared to Gemini 3.1 Pro's 88/100 in coding and 89/100 in reasoning.
Capability breakdown
Across the five core benchmark categories, here is how GPT-5.6 Terra and Gemini 3.1 Pro stack up head to head:
Best model by task
- coding: GPT-5.6 Terra wins with 93/100
- reasoning: GPT-5.6 Terra wins with 94/100
- data extraction: GPT-5.6 Terra wins with 93/100
- creative tasks: GPT-5.6 Terra wins with 92/100
- vision/multimodal: Gemini 3.1 Pro wins with 95/100
Estimated monthly cost at scale
At 10M + 2M per month, GPT-5.6 Terra runs about $44.00 while Gemini 3.1 Pro runs about $44.00 — GPT-5.6 Terra saves roughly $0.00 (0%) every month.
What actually decides it
GPT-5.6 Terra and Gemini 3.1 Pro come from different labs, which means different tokenizers, different API shapes, and a second vendor relationship. The same English text does not produce the same token count on both, so a price-per-million comparison understates the difference — measure your own prompts on each before treating the headline rates as the full story.
GPT-5.6 Terra and Gemini 3.1 Pro were released within 3 months of each other, so they are competing on the same evaluations under roughly the same conditions. That makes a direct benchmark comparison meaningful here in a way it usually is not — neither model has the advantage of being measured on a newer, easier set of tests.
Gemini 3.1 Pro carries the larger context window at 2.0M tokens versus 1.1M for GPT-5.6 Terra. The gap is real but not decisive — it matters if your prompts routinely run long, and is irrelevant if they sit where most production prompts sit, well under 100K. Bear in mind that filling a large window is also what makes a request expensive.
Throughput is close enough to ignore — 86/100 versus 82/100. Neither model will feel noticeably quicker in an interactive product, so latency is not a reason to choose between them.
One practical asymmetry: GPT-5.6 Terra offers a batch API at 50% off standard rates, and Gemini 3.1 Pro does not. For anything that does not need an answer immediately — nightly enrichment, backfills, evaluation runs — that discount can be worth more than the difference in list price, and it is easy to overlook when comparing headline rates.
With prices this close (0% apart), pick on fit rather than cost. GPT-5.6 Terra holds the benchmark edge; weigh that against where each one is weakest — creative tasks and coding respectively — and use the calculator above with your own token mix.
Benchmark Comparison
Head-to-head scores across 5 categories — sourced from official evals
Coding
Reasoning
Extraction
Creative
Vision
Speed Score
Context Window
What Is a Token?
Models don't read words — they process tokens.
A token is roughly 4 characters of English text (~¾ of a word). Your API bill is priced per million tokens — understanding this directly reduces your costs.
Short phrase
"Hello, world!"
- GPT-5.6 Terra
- $0.80
- Gemini 3.1 Pro
- $0.78
Business email
One typical email (~200 words)
- GPT-5.6 Terra
- $54.00
- Gemini 3.1 Pro
- $52.38
Code file
50-line Python script
- GPT-5.6 Terra
- $80.00
- Gemini 3.1 Pro
- $77.60
Prices shown are for 100,000 runs of each workload — one run costs a fraction of a cent on both GPT-5.6 Terra and Gemini 3.1 Pro, so the number only becomes meaningful at production volume. Input tokens only; add your output volume in the calculator below.
How to check your token usage
response.usage.total_tokensEvery API response includes a usage object. Sum total_tokens across all calls to get your monthly figure, then use the calculator below.
Your Cost Calculator
Enter your actual monthly token usage to see real savings
Quick Presets
GPT-5.6 Terra
$150.00/mo
$1,800.00/yr
Gemini 3.1 Pro
$150.00/mo
$1,800.00/yr
Deep-Dive Audit — GPT-5.6 Terra & Gemini 3.1 Pro
Surgically Auditing: Deep Logic
3-YEAR STRATEGIC LOSS PROJECTION
$415.404
Without optimization protocols, current model choices will result in $138.468 capital loss per year.
EFFICIENCY SCORE
94%
This model achieves a 94 benchmark score in this category.
CATEGORY GAP
6 pts
Distance from Leader
Competitive Landscape Analysis
Source: MMLU-Pro + GPQA Diamond (Apr 2026)
Category Champion: Claude Fable 5
According to MMLU-Pro + GPQA Diamond (Apr 2026) data, Claude Fable 5 provides the optimum balance for Deep Logic tasks.
Market Score
%100
Savings Rate
%82
Operational Prescription
- Implement model cascading to optimize token spend.
- Analyze complex_reasoning data to leverage local semantic caching.
COST AUDIT PROTOCOL
Overkill Detected
"GPT-5.6 Terra is overpriced for this task type. Claude Fable 5 scores 100 in this category at a fraction of the cost."
Categorical Alternative Opportunity
"Claude Fable 5 leads this category with 100 points according to MMLU-Pro + GPQA Diamond (Apr 2026) data."
Inertia Tax Detected
"85% of traffic can be routed to cheaper models. Fast tier (GPT-5 Nano) and Smart tier (o3-mini) can save $11.54/month."
3-Tier Intelligent Routing Architecture
82% SAVINGS VIA ROUTINGGPT-5 Nano
IQ Score: 72/100
$18.00/yr
o3-mini
IQ Score: 97/100
$277.20/yr
DeepSeek R1
IQ Score: 97/100
$59.184/yr
Without tiered routing, you pay the 'Inertia Tax' — routing all traffic to the most expensive model regardless of task complexity. Tiered cascade eliminates $1,661.616/year in avoidable overhead.
Deep Logic — Model Cost / Quality Matrix
Source: MMLU-Pro + GPQA Diamond (Apr 2026)| Model | Benchmark | Input (per M) | Output (per M) | Annual Cost* | Value Index |
|---|---|---|---|---|---|
o3-miniBEST VALUE | 97/100 | $1.10 | $4.40 | $66.00 | 100/100 |
GPT-5.2 Chat | 96/100 | $1.75 | $14.00 | $189.00 | 35/100 |
Claude 3.7 Sonnet | 95/100 | $3.00 | $15.00 | $216.00 | 30/100 |
GPT-5.6 TerraSELECTED | 94/100 | $2.00 | $12.00 | $168.00 | 38/100 |
Claude 3.5 Sonnet | 93/100 | $3.00 | $15.00 | $216.00 | 29/100 |
GPT-4.1 | 93/100 | $2.00 | $8.00 | $120.00 | 53/100 |
Claude Sonnet 5 | 92/100 | $3.00 | $15.00 | $216.00 | 29/100 |
Grok 4.5 | 90/100 | $2.00 | $6.00 | $96.00 | 64/100 |
GPT-4o | 90/100 | $2.50 | $10.00 | $150.00 | 41/100 |
Gemini 3.1 Pro | 89/100 | $2.00 | $12.00 | $168.00 | 36/100 |
Gemini 2.0 Pro | 88/100 | $1.25 | $5.00 | $75.00 | 80/100 |
Gemini 1.5 Pro | 87/100 | $1.25 | $5.00 | $75.00 | 79/100 |
Mistral Large 2 | 86/100 | $2.00 | $6.00 | $96.00 | 61/100 |
* Annual cost for given volumes. Value Index = Score / Cost (Higher = Best Value).
// iOPTERA Surgical Routing Wrapper
const auditModel = async (prompt: string) => {
const complexity = measureComplexity(prompt);
// Tactical Cascade Logic
if (complexity < 0.45) {
// Redirect simple tasks to efficient model
return await llm.call("iOPTERA Optimization", prompt);
}
// High-latency routing for complex reasoning
return await llm.call("Claude Fable 5", prompt);
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